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L2 Regularization for all layers #131

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L2 Regularization for all layers #131

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aa81b09
L2 Regularization for all layers
aayux Jan 4, 2018
a900598
Update text_cnn.py
aayux Jan 29, 2018
6632ebb
Update text_cnn.py
aayux Mar 8, 2018
65c2862
Update text_cnn.py
aayux Mar 10, 2018
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8 changes: 5 additions & 3 deletions text_cnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@ def __init__(

# Keeping track of l2 regularization loss (optional)
l2_loss = tf.constant(0.0)

# Embedding layer
with tf.device('/cpu:0'), tf.name_scope("embedding"):
self.W = tf.Variable(
Expand Down Expand Up @@ -68,11 +68,13 @@ def __init__(
shape=[num_filters_total, num_classes],
initializer=tf.contrib.layers.xavier_initializer())
b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")
l2_loss += tf.nn.l2_loss(W)
l2_loss += tf.nn.l2_loss(b)
self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name="scores")
self.predictions = tf.argmax(self.scores, 1, name="predictions")

# Calculate L2 Regularization
if l2_reg_lambda > 0.:
l2_loss = tf.add_n([tf.nn.l2_loss(var) for var in tf.trainable_variables()[1:] if "W" in var.name])

# Calculate mean cross-entropy loss
with tf.name_scope("loss"):
losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)
Expand Down